Recent work reported the label alignment property in a supervised learning setting: the vector of all labels in the dataset is mostly in the span of the top few singular vectors of the data matrix. Inspired by this observation, we derive a regularization method for unsupervised domain adaptation. Instead of regularizing representation learning as done by popular domain adaptation methods, we regularize the classifier so that the target domain predictions can to some extent ``align" with the top singular vectors of the unsupervised data matrix from the target domain. In a linear regression setting, we theoretically justify the label alignment property and characterize the optimality of the solution of our regularization by bounding its distance to the optimal solution. We conduct experiments to show that our method can work well on the label shift problems, where classic domain adaptation methods are known to fail. We also report mild improvement over domain adaptation baselines on a set of commonly seen MNIST-USPS domain adaptation tasks and on cross-lingual sentiment analysis tasks.

使用DataSet的真实标签培训而不是随机标签导致更快的优化和更好的泛化。这种差异归因于自然数据集中的输入和标签之间的对齐概念。我们发现，随机或真正标签上的具有不同架构和优化器的培训神经网络在隐藏的表示和训练标签之间强制执行相同的关系，阐明为什么神经网络表示为转移如此成功。我们首先突出显示为什么对齐的特征在经典的合成转移问题中促进转移和展示，即对齐是对相似和不同意任务的正负传输的确定因素。然后我们调查各种神经网络架构，并发现（a）在各种不同的架构和优化器中出现的对齐，并且从深度（b）对准产生的更多对准对于更接近输出的层和（c）现有的性能深度CNN表现出高级别的对准。

Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hope is that the learnt representation, together with the hypothesis learnt from the source domain, can generalize to the target domain. In this paper, we first construct a simple counterexample showing that, contrary to common belief, the above conditions are not sufficient to guarantee successful domain adaptation. In particular, the counterexample exhibits conditional shift: the class-conditional distributions of input features change between source and target domains. To give a sufficient condition for domain adaptation, we propose a natural and interpretable generalization upper bound that explicitly takes into account the aforementioned shift. Moreover, we shed new light on the problem by proving an information-theoretic lower bound on the joint error of any domain adaptation method that attempts to learn invariant representations. Our result characterizes a fundamental tradeoff between learning invariant representations and achieving small joint error on both domains when the marginal label distributions differ from source to target. Finally, we conduct experiments on real-world datasets that corroborate our theoretical findings. We believe these insights are helpful in guiding the future design of domain adaptation and representation learning algorithms.

Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being "frustratingly easy" to implement. However, in practice, the target domain is often unlabeled, requiring unsupervised adaptation. We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Even though it is extraordinarily simple-it can be implemented in four lines of Matlab code-CORAL performs remarkably well in extensive evaluations on standard benchmark datasets."Everything should be made as simple as possible, but not simpler."

We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages.We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application.

所有著名的机器学习算法构成了受监督和半监督的学习工作，只有在一个共同的假设下：培训和测试数据遵循相同的分布。当分布变化时，大多数统计模型必须从新收集的数据中重建，对于某些应用程序，这些数据可能是昂贵或无法获得的。因此，有必要开发方法，以减少在相关领域中可用的数据并在相似领域中进一步使用这些数据，从而减少需求和努力获得新的标签样品。这引起了一个新的机器学习框架，称为转移学习：一种受人类在跨任务中推断知识以更有效学习的知识能力的学习环境。尽管有大量不同的转移学习方案，但本调查的主要目的是在特定的，可以说是最受欢迎的转移学习中最受欢迎的次级领域，概述最先进的理论结果，称为域适应。在此子场中，假定数据分布在整个培训和测试数据中发生变化，而学习任务保持不变。我们提供了与域适应性问题有关的现有结果的首次最新描述，该结果涵盖了基于不同统计学习框架的学习界限。

我们考虑无监督的域适应性（UDA），其中使用来自源域（例如照片）的标记数据，而来自目标域（例如草图）的未标记数据用于学习目标域的分类器。常规的UDA方法（例如，域对抗训练）学习域不变特征，以改善对目标域的概括。在本文中，我们表明，对比的预训练，它在未标记的源和目标数据上学习功能，然后在标记的源数据上进行微调，具有强大的UDA方法的竞争力。但是，我们发现对比前训练不会学习域不变特征，这与常规的UDA直觉不同。从理论上讲，我们证明了对比的预训练可以学习在跨域下微调但仍通过解开域和类信息来概括到目标域的特征。我们的结果表明，UDA不需要域的不变性。我们从经验上验证了基准视觉数据集的理论。

Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned representations should also be discriminative in prediction. To learn such representations, domain adaptation frameworks usually include a domain invariant representation learning approach to measure and reduce the domain discrepancy, as well as a discriminator for classification. Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WD-GRL). WDGRL utilizes a neural network, denoted by the domain critic, to estimate empirical Wasserstein distance between the source and target samples and optimizes the feature extractor network to minimize the estimated Wasserstein distance in an adversarial manner. The theoretical advantages of Wasserstein distance for domain adaptation lie in its gradient property and promising generalization bound. Empirical studies on common sentiment and image classification adaptation datasets demonstrate that our proposed WDGRL outperforms the state-of-the-art domain invariant representation learning approaches.

无监督域适应（UDA）的绝大多数现有算法都集中在以一次性的方式直接从标记的源域调整到未标记的目标域。另一方面，逐渐的域适应性（GDA）假设桥接源和目标的$（t-1）$未标记的中间域，并旨在通过利用中间的路径在目标域中提供更好的概括。在某些假设下，Kumar等人。 （2020）提出了一种简单的算法，逐渐自我训练，以及按$ e^{o（t）} \ left的顺序结合的概括（\ varepsilon_0+o \ of \ left（\ sqrt {log（log（log（t）/n log（t）/n） } \ right）\ right）$对于目标域错误，其中$ \ varepsilon_0 $是源域错误，$ n $是每个域的数据大小。由于指数因素，当$ t $仅适中时，该上限变得空虚。在这项工作中，我们在更一般和放松的假设下分析了逐步的自我训练，并证明概括为$ \ varepsilon_0 + o \ left（t \ delta + t/\ sqrt {n} {n} \ right） + \ widetilde { o} \ left（1/\ sqrt {nt} \ right）$，其中$ \ delta $是连续域之间的平均分配距离。与对$ t $作为乘法因素的指数依赖性的现有界限相比，我们的界限仅取决于$ t $线性和添加性。也许更有趣的是，我们的结果意味着存在最佳的$ t $的最佳选择，从而最大程度地减少了概括性错误，并且自然也暗示了一种构造中间域路径的最佳方法，以最大程度地减少累积路径长度$ t \ delta源和目标之间的$。为了证实我们理论的含义，我们检查了对多个半合成和真实数据集的逐步自我训练，这证实了我们的发现。我们相信我们的见解为未来GDA算法设计的途径提供了前进的途径。

The cost of large scale data collection and annotation often makes the application of machine learning algorithms to new tasks or datasets prohibitively expensive. One approach circumventing this cost is training models on synthetic data where annotations are provided automatically. Despite their appeal, such models often fail to generalize from synthetic to real images, necessitating domain adaptation algorithms to manipulate these models before they can be successfully applied. Existing approaches focus either on mapping representations from one domain to the other, or on learning to extract features that are invariant to the domain from which they were extracted. However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain. We suggest that explicitly modeling what is unique to each domain can improve a model's ability to extract domain-invariant features. Inspired by work on private-shared component analysis, we explicitly learn to extract image representations that are partitioned into two subspaces: one component which is private to each domain and one which is shared across domains. Our model is trained not only to perform the task we care about in the source domain, but also to use the partitioned representation to reconstruct the images from both domains. Our novel architecture results in a model that outperforms the state-of-the-art on a range of unsupervised domain adaptation scenarios and additionally produces visualizations of the private and shared representations enabling interpretation of the domain adaptation process.

域适应性是现代机器学习中的一种流行范式，旨在解决培训或验证数据集之间具有用于学习和测试分类器（源域）和潜在的大型未标记数据集的培训或验证数据集之间的分歧问题，其中利用了模型（目标域）（目标域）（目标域） 。任务是找到源数据集的源和目标数据集的这种常见表示，其中源数据集提供了培训的信息，因此可以最大程度地减少来源和目标之间的差异。目前，最流行的领域适应性解决方案是基于训练神经网络，这些神经网络结合了分类和对抗性学习模块，这些模块是饥饿的，通常很难训练。我们提出了一种称为域适应性主成分分析（DAPCA）的方法，该方法发现线性减少的数据表示有助于解决域适应任务。 DAPCA基于数据点对之间引入正权重，并概括了主成分分析的监督扩展。 DAPCA代表一种迭代算法，因此在每次迭代中都解决了一个简单的二次优化问题。保证算法的收敛性，并且在实践中的迭代次数很少。我们验证了先前提出的用于解决域适应任务的基准的建议算法，还显示了在生物医学应用中对单细胞法数据集进行分析中使用DAPCA的好处。总体而言，考虑到源域和目标域之间可能的差异，DAPCA可以作为许多机器学习应用程序中有用的预处理步骤。

Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled targetdomain data is necessary).As the training progresses, the approach promotes the emergence of "deep" features that are (i) discriminative for the main learning task on the source domain and (ii) invariant with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a simple new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation.Overall, the approach can be implemented with little effort using any of the deep-learning packages. The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-ofthe-art on Office datasets.

Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially-costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions.

虽然在许多域内生成并提供了大量的未标记数据，但对视觉数据的自动理解的需求高于以往任何时候。大多数现有机器学习模型通常依赖于大量标记的训练数据来实现高性能。不幸的是，在现实世界的应用中，不能满足这种要求。标签的数量有限，手动注释数据昂贵且耗时。通常需要将知识从现有标记域传输到新域。但是，模型性能因域之间的差异（域移位或数据集偏差）而劣化。为了克服注释的负担，域适应（DA）旨在在将知识从一个域转移到另一个类似但不同的域中时减轻域移位问题。无监督的DA（UDA）处理标记的源域和未标记的目标域。 UDA的主要目标是减少标记的源数据和未标记的目标数据之间的域差异，并在培训期间在两个域中学习域不变的表示。在本文中，我们首先定义UDA问题。其次，我们从传统方法和基于深度学习的方法中概述了不同类别的UDA的最先进的方法。最后，我们收集常用的基准数据集和UDA最先进方法的报告结果对视觉识别问题。

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to a different but related unlabeled target domain with identical label space. Currently, the main workhorse for solving UDA is domain alignment, which has proven successful. However, it is often difficult to find an appropriate source domain with identical label space. A more practical scenario is so-called partial domain adaptation (PDA) in which the source label set or space subsumes the target one. Unfortunately, in PDA, due to the existence of the irrelevant categories in the source domain, it is quite hard to obtain a perfect alignment, thus resulting in mode collapse and negative transfer. Although several efforts have been made by down-weighting the irrelevant source categories, the strategies used tend to be burdensome and risky since exactly which irrelevant categories are unknown. These challenges motivate us to find a relatively simpler alternative to solve PDA. To achieve this, we first provide a thorough theoretical analysis, which illustrates that the target risk is bounded by both model smoothness and between-domain discrepancy. Considering the difficulty of perfect alignment in solving PDA, we turn to focus on the model smoothness while discard the riskier domain alignment to enhance the adaptability of the model. Specifically, we instantiate the model smoothness as a quite simple intra-domain structure preserving (IDSP). To our best knowledge, this is the first naive attempt to address the PDA without domain alignment. Finally, our empirical results on multiple benchmark datasets demonstrate that IDSP is not only superior to the PDA SOTAs by a significant margin on some benchmarks (e.g., +10% on Cl->Rw and +8% on Ar->Rw ), but also complementary to domain alignment in the standard UDA

无监督的域适应（UDA）通过将知识从标记的源域传送到与目标的分布不同的标记源域来实现跨域学习。但是，UDA并不总是成功，在文献中报告了几个“负转移”的几个账目。在这项工作中，我们在目标域错误上证明了一个简单的下限，这些错误符合现有的上限。我们的界定显示了最小化源域误差和边际分布不匹配的不足，因为由于可能的诱导标记功能不匹配可能增加，因此由于可能的增加而减少目标域误差。通过同一UDA方法成功，失败的简单分布进一步说明了这种不足，并且可以成功或失败，并且可以使用相同的机会。从此激励，我们提出了新的数据中毒攻击，以欺骗UDA方法进入产生大目标域错误的学习陈述。我们使用基准数据集评估这些攻击对流行的UDA方法的影响，他们以前已经证明是成功的。我们的结果表明，中毒可以显着降低目标域精度，在某些情况下将其降至近0％，在源域中添加了10％中毒数据。这些UDA方法的失败在保证与我们下限符合的跨域泛化时，他们的局限性阐述了它们的局限性。因此，评估诸如数据中毒等对逆势设置中的UDA方法提供了更好的稳健性对UDA不利的数据分布。

变异因素之间的相关性在现实数据中普遍存在。机器学习算法可能会受益于利用这种相关性，因为它们可以提高噪声数据的预测性能。然而，通常这种相关性不稳定（例如，它们可能在域，数据集或应用程序之间发生变化），我们希望避免利用它们。解剖学方法旨在学习捕获潜伏子空间变化不同因素的表示。常用方法涉及最小化潜伏子空间之间的相互信息，使得每个潜在的底层属性。但是，当属性相关时，这会失败。我们通过强制执行可用属性上的子空间之间的独立性来解决此问题，这允许我们仅删除不导致的依赖性，这些依赖性是由于训练数据中存在的相关结构。我们通过普发的方法实现这一目标，以最小化关于分类变量的子空间之间的条件互信息（CMI）。我们首先在理论上展示了CMI最小化是对高斯数据线性问题的稳健性解剖的良好目标。然后，我们基于MNIST和Celeba在现实世界数据集上应用我们的方法，并表明它会在相关偏移下产生脱屑和强大的模型，包括弱监督设置。

Discriminative learning methods for classification perform well when training and test data are drawn from the same distribution. Often, however, we have plentiful labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and little or no labeled training data. In this work we investigate two questions. First, under what conditions can a classifier trained from source data be expected to perform well on target data? Second, given a small amount of labeled target data, how should we combine it during training with the large amount of labeled source data to achieve the lowest target error at test time?

解决无监督域的适应性的主要方法是将源和目标域的数据点映射到嵌入式空间中，该空间被建模为共享深层编码器的输出空间。对编码器进行了训练，以使嵌入式空间域 - 敏捷剂，以使源训练的分类器可在目标域上推广。进一步提高UDA性能的次要机制是使源域分布更加紧凑，以提高模型的通用性。我们证明，增加嵌入空间中的阶级边缘可以帮助开发具有改善性能的UDA算法。我们估计源域的内部学习的多模式分布，该分布是由于预处理而学到的，并使用它来增加源域中的类间分离以减少域移位的效果。我们证明，使用我们的方法导致在四个标准基准UDA图像分类数据集上提高模型的通用性，并与退出方法进行了有利的比较。

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning researches, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey paper reviews more than forty representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments. The models are performed on three different datasets, i.e., Amazon Reviews, Reuters-21578, and Office-31. And the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.